How does MCP-ORTools work?

You specify your problem in JSON format, defining variables, constraints (using OR-Tools syntax), and an optional objective function. MCP-ORTools then uses OR-Tools to find optimal or feasible solutions, returning the results in a JSON format.

What types of problems can I solve with MCP-ORTools?

MCP-ORTools is suitable for a wide range of constraint satisfaction and optimization problems, including knapsack problems, portfolio selection, scheduling, resource allocation, and any scenario where you need to find the best solution under certain constraints.

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Ortools MCP Server

Integrates Google's OR-Tools constraint programming solver with Large Language Models using the Model Context Protocol for AI models.

Local serverstdioPython★ 19

What is the Ortools MCP Server?

Integrates Google's OR-Tools constraint programming solver with Large Language Models using the Model Context Protocol for AI models.

What the server does

A Model Context Protocol (MCP) server implementation using Google OR-Tools for constraint solving. Designed for use with Large Language Models through standardized constraint model specification.

Full OR-Tools CP-SAT solver support
JSON-based model specification
Integer and boolean variables (domain: [min, max])
Linear constraints using OR-Tools method syntax
Linear optimization objectives
Timeouts and solver parameters

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

How to install the Ortools MCP Server

{
  "mcpServers": {
    "ortools": {
      "command": "uvx",
      "args": ["git"]
    }
  }
}

Add to claude_desktop_config.json, then restart Claude Desktop.

Frequently asked questions

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models (LLMs) via the Model Context Protocol (MCP), enabling LLMs to solve complex optimization and constraint satisfaction problems.